Investor Sentiment and Chinese A-Share Stock Markets Anomalies
Bibliographic record
Abstract
This paper creates an investor sentiment index for the Chinese A-share stock market. We document the details of this index and use it to explain about asset pricing anomalies in the Chinese stock markets. We test the effect of investor sentiment on 13 asset pricing anomalies in the Chinese stock markets. Out of the 13 anomalies, 9 of them are significantly affected by investor sentiment. In particular, the factors of firm size (Size), total risk (Sigma), stock issuance growth (Issue), total accruals (Accruals), net operating assets (Opa), profit premium (Profit), growth of assets (GA), return on assets (ROA), and return on equities (ROE) are significantly positive, which mean that there are positive relations between market abnormal returns with lagged investor sentiment. Therefore, following high investor sentiment, the profits from a long-short strategy will be more and short leg portfolios will mostly provide gains at the same time. We consider the findings of this study to be not only an important supplementary of the Chinese A-share stock market to the existing theories on global investor sentiment, but also efficient strategies for investors to determine the movement of stock returns and make their investing decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".